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Modelling of surface and shallow subsurface data is getting more and more advanced and is demonstrated mostly for onshore (hydro)geological applications. Three-dimensional (3D) modelling techniques are used increasingly, and now include voxel modelling that often employs stochastic or probabilistic methods to assess model uncertainty. This paper presents an adapted methodological workflow for the 3D modelling of offshore sand deposits and aims at demonstrating the improvement of the estimations of lithological properties after incorporation of more geological layers in the modelling process. Importantly, this process is driven by new geological insight from the combined interpretation of seismic and borehole data. Applying 3D modelling techniques is challenging given that offshore environments may be heavily reworked through time, often leading to thin and discontinuous deposits. Since voxel and stochastic modelling allow in-depth analyses of a multitude of properties (and their associated uncertainties) that define a lithological layer, they are ideal for use in an aggregate resource exploitation context. The voxel model is now the backbone of a decision support system for long-term sand extraction on the Belgian Continental Shelf.
The urbanised peat-rich coastal-deltaic plain of the Netherlands is severely subsiding due to human-induced phreatic groundwater level lowering, as this causes peat layers to compress and oxidise. To determine the potential susceptibility of this area to future subsidence by peat compression and oxidation, the effects of lowering present-day phreatic groundwater levels were quantitatively evaluated using a subsidence model. Input were a 3D geological subsurface voxel-model, modelled phreatic groundwater levels, and functions for peat compression and oxidation. Phreatic groundwater levels were lowered by 0.25 and 0.5m, and the resulting peat compression and oxidation over periods of 15 and 30 years were determined. The model area comprised the major cities Amsterdam and Rotterdam, and their surrounding agricultural lands.
The results revealed that for these scenarios agricultural areas may subside between 0.3 and 0.8m; potential subsidence in Amsterdam and Rotterdam is considerably lower, less than 0.4m. This is due to the presence of several metres thick anthropogenic brought-up soils overlying the peat below the urban areas, which has already compressed the peat to a depth below groundwater level, and thus minimises further compression and oxidation. In agricultural areas peat is often situated near the surface, and is therefore highly compressible and prone to oxidation. The averaged subsidence rates for the scenarios range between 7 and 13mma−1, which is corresponds to present-day rates of subsidence in the peat areas of the Netherlands. These results contrast with the trend of coastal-deltaic subsidence in other deltas, with cities subsiding faster than agricultural areas. This difference is explained by the driver of subsidence: in other deltas, subsidence of urban areas is mainly due to deep aquifer extraction, whereas in the Netherlands subsidence is due to phreatic groundwater level lowering.
The shallow subsurface of Groningen, the Netherlands, is heterogeneous due to its formation in a Holocene tidal coastal setting on a periglacially and glacially inherited landscape with strong lateral variation in subsurface architecture. Soft sediments with low, small-strain shear wave velocities (VS30 around 200 m s−1) are known to amplify earthquake motions. Knowledge of the architecture and properties of the subsurface and the combined effect on the propagation of earthquake waves is imperative for the prediction of geohazards of ground shaking and liquefaction at the surface. In order to provide information for the seismic hazard and risk analysis, two geological models were constructed. The first is the ‘Geological model for Site response in Groningen’ (GSG model) and is based on the detailed 3D GeoTOP voxel model containing lithostratigraphy and lithoclass attributes. The GeoTOP model was combined with information from boreholes, cone penetration tests, regional digital geological and geohydrological models to cover the full range from the surface down to the base of the North Sea Supergroup (base Paleogene) at ~800 m depth. The GSG model consists of a microzonation based on geology and a stack of soil stratigraphy for each of the 140,000 grid cells (100 m × 100 m) to which properties (VS and parameters relevant for nonlinear soil behaviour) were assigned. The GSG model serves as input to the site response calculations that feed into the Ground Motion Model. The second model is the ‘Geological model for Liquefaction sensitivity in Groningen’ (GLG). Generally, loosely packed sands might be susceptible to liquefaction upon earthquake shaking. In order to delineate zones of loosely packed sand in the first 40 m below the surface, GeoTOP was combined with relative densities inferred from a large cone penetration test database. The marine Naaldwijk and Eem Formations have the highest proportion of loosely packed sand (31% and 38%, respectively) and thus are considered to be the most vulnerable to liquefaction; other units contain 5–17% loosely packed sand. The GLG model serves as one of the inputs for further research on the liquefaction potential in Groningen, such as the development of region-specific magnitude scaling factors (MSF) and depth–stress reduction relationships (rd).
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